Overview

Dataset statistics

Number of variables27
Number of observations20631
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory249.6 B

Variable types

Numeric19
Categorical8

Alerts

OS_3 has constant value "100.0"Constant
sensor_1 has constant value "518.67"Constant
sensor_5 has constant value "14.62"Constant
sensor_10 has constant value "1.3"Constant
sensor_16 has constant value "0.03"Constant
sensor_18 has constant value "2388"Constant
sensor_19 has constant value "100.0"Constant
time_cycles is highly overall correlated with sensor_2 and 12 other fieldsHigh correlation
sensor_2 is highly overall correlated with time_cycles and 12 other fieldsHigh correlation
sensor_3 is highly overall correlated with time_cycles and 12 other fieldsHigh correlation
sensor_4 is highly overall correlated with time_cycles and 13 other fieldsHigh correlation
sensor_7 is highly overall correlated with time_cycles and 12 other fieldsHigh correlation
sensor_8 is highly overall correlated with sensor_2 and 13 other fieldsHigh correlation
sensor_9 is highly overall correlated with time_cycles and 5 other fieldsHigh correlation
sensor_11 is highly overall correlated with time_cycles and 13 other fieldsHigh correlation
sensor_12 is highly overall correlated with time_cycles and 12 other fieldsHigh correlation
sensor_13 is highly overall correlated with sensor_2 and 13 other fieldsHigh correlation
sensor_14 is highly overall correlated with time_cycles and 7 other fieldsHigh correlation
sensor_15 is highly overall correlated with time_cycles and 12 other fieldsHigh correlation
sensor_17 is highly overall correlated with time_cycles and 14 other fieldsHigh correlation
sensor_20 is highly overall correlated with time_cycles and 12 other fieldsHigh correlation
sensor_21 is highly overall correlated with time_cycles and 12 other fieldsHigh correlation
RUL is highly overall correlated with time_cycles and 14 other fieldsHigh correlation
OS_1 has 413 (2.0%) zerosZeros
OS_2 has 2070 (10.0%) zerosZeros

Reproduction

Analysis started2022-12-23 11:15:40.669301
Analysis finished2022-12-23 11:21:36.628806
Duration5 minutes and 55.96 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Unit_number
Real number (ℝ)

Distinct100
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.506568
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:41.345921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q126
median52
Q377
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.227633
Coefficient of variation (CV)0.56745449
Kurtosis-1.2198241
Mean51.506568
Median Absolute Deviation (MAD)26
Skewness-0.067815234
Sum1062632
Variance854.25453
MonotonicityIncreasing
2022-12-23T16:51:41.727322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 362
 
1.8%
92 341
 
1.7%
96 336
 
1.6%
67 313
 
1.5%
83 293
 
1.4%
2 287
 
1.4%
95 283
 
1.4%
64 283
 
1.4%
86 278
 
1.3%
17 276
 
1.3%
Other values (90) 17579
85.2%
ValueCountFrequency (%)
1 192
0.9%
2 287
1.4%
3 179
0.9%
4 189
0.9%
5 269
1.3%
6 188
0.9%
7 259
1.3%
8 150
0.7%
9 201
1.0%
10 222
1.1%
ValueCountFrequency (%)
100 200
1.0%
99 185
0.9%
98 156
0.8%
97 202
1.0%
96 336
1.6%
95 283
1.4%
94 258
1.3%
93 155
0.8%
92 341
1.7%
91 135
 
0.7%

time_cycles
Real number (ℝ)

Distinct362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.80786
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:42.242931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q152
median104
Q3156
95-th percentile230
Maximum362
Range361
Interquartile range (IQR)104

Descriptive statistics

Standard deviation68.88099
Coefficient of variation (CV)0.63305159
Kurtosis-0.2185391
Mean108.80786
Median Absolute Deviation (MAD)52
Skewness0.49990397
Sum2244815
Variance4744.5908
MonotonicityNot monotonic
2022-12-23T16:51:42.519898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
0.5%
66 100
 
0.5%
97 100
 
0.5%
96 100
 
0.5%
95 100
 
0.5%
94 100
 
0.5%
93 100
 
0.5%
91 100
 
0.5%
90 100
 
0.5%
89 100
 
0.5%
Other values (352) 19631
95.2%
ValueCountFrequency (%)
1 100
0.5%
2 100
0.5%
3 100
0.5%
4 100
0.5%
5 100
0.5%
6 100
0.5%
7 100
0.5%
8 100
0.5%
9 100
0.5%
10 100
0.5%
ValueCountFrequency (%)
362 1
< 0.1%
361 1
< 0.1%
360 1
< 0.1%
359 1
< 0.1%
358 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
355 1
< 0.1%
354 1
< 0.1%
353 1
< 0.1%

OS_1
Real number (ℝ)

Distinct158
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.8701469 × 10-6
Minimum-0.0087
Maximum0.0087
Zeros413
Zeros (%)2.0%
Negative10061
Negative (%)48.8%
Memory size322.4 KiB
2022-12-23T16:51:42.847454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0087
5-th percentile-0.0037
Q1-0.0015
median0
Q30.0015
95-th percentile0.0036
Maximum0.0087
Range0.0174
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0021873134
Coefficient of variation (CV)-246.5927
Kurtosis-0.0091316243
Mean-8.8701469 × 10-6
Median Absolute Deviation (MAD)0.0015
Skewness-0.024766267
Sum-0.183
Variance4.7843401 × 10-6
MonotonicityNot monotonic
2022-12-23T16:51:43.456296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
2.0%
0.0002 398
 
1.9%
0.0004 394
 
1.9%
-0.0005 390
 
1.9%
0.0001 382
 
1.9%
0.0005 381
 
1.8%
0.0006 379
 
1.8%
-0.0006 375
 
1.8%
0.0003 364
 
1.8%
0.0009 362
 
1.8%
Other values (148) 16793
81.4%
ValueCountFrequency (%)
-0.0087 1
 
< 0.1%
-0.0086 1
 
< 0.1%
-0.0084 1
 
< 0.1%
-0.0081 2
< 0.1%
-0.0078 1
 
< 0.1%
-0.0075 1
 
< 0.1%
-0.0074 3
< 0.1%
-0.0073 1
 
< 0.1%
-0.0072 2
< 0.1%
-0.007 2
< 0.1%
ValueCountFrequency (%)
0.0087 1
 
< 0.1%
0.0083 1
 
< 0.1%
0.0077 1
 
< 0.1%
0.0076 1
 
< 0.1%
0.0074 3
< 0.1%
0.0073 1
 
< 0.1%
0.0072 4
< 0.1%
0.0071 2
< 0.1%
0.007 2
< 0.1%
0.0069 2
< 0.1%

OS_2
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3508313 × 10-6
Minimum-0.0006
Maximum0.0006
Zeros2070
Zeros (%)10.0%
Negative9225
Negative (%)44.7%
Memory size322.4 KiB
2022-12-23T16:51:43.753152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0006
5-th percentile-0.0004
Q1-0.0002
median0
Q30.0003
95-th percentile0.0005
Maximum0.0006
Range0.0012
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.00029306212
Coefficient of variation (CV)124.66319
Kurtosis-1.130447
Mean2.3508313 × 10-6
Median Absolute Deviation (MAD)0.0003
Skewness0.0090851197
Sum0.0485
Variance8.5885409 × 10-8
MonotonicityNot monotonic
2022-12-23T16:51:43.973397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
-0.0003 2104
10.2%
0.0001 2097
10.2%
0 2070
10.0%
0.0003 2065
10.0%
-0.0004 2051
9.9%
-0.0002 2049
9.9%
0.0002 2038
9.9%
-0.0001 2029
9.8%
0.0004 1997
9.7%
0.0005 1068
5.2%
Other values (3) 1063
5.2%
ValueCountFrequency (%)
-0.0006 34
 
0.2%
-0.0005 958
4.6%
-0.0004 2051
9.9%
-0.0003 2104
10.2%
-0.0002 2049
9.9%
-0.0001 2029
9.8%
0 2070
10.0%
0.0001 2097
10.2%
0.0002 2038
9.9%
0.0003 2065
10.0%
ValueCountFrequency (%)
0.0006 71
 
0.3%
0.0005 1068
5.2%
0.0004 1997
9.7%
0.0003 2065
10.0%
0.0002 2038
9.9%
0.0001 2097
10.2%
0 2070
10.0%
-0.0001 2029
9.8%
-0.0002 2049
9.9%
-0.0003 2104
10.2%

OS_3
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.4 KiB
100.0
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters103155
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 20631
100.0%

Length

2022-12-23T16:51:44.270252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:51:44.693400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82524
80.0%
Other Punctuation 20631
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61893
75.0%
1 20631
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

sensor_1
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.4 KiB
518.67
20631 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters123786
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row518.67
2nd row518.67
3rd row518.67
4th row518.67
5th row518.67

Common Values

ValueCountFrequency (%)
518.67 20631
100.0%

Length

2022-12-23T16:51:44.892448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:51:45.131853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
518.67 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103155
83.3%
Other Punctuation 20631
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 20631
20.0%
1 20631
20.0%
8 20631
20.0%
6 20631
20.0%
7 20631
20.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%

sensor_2
Real number (ℝ)

Distinct310
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.68093
Minimum641.21
Maximum644.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:45.350588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum641.21
5-th percentile641.92
Q1642.325
median642.64
Q3643
95-th percentile643.58
Maximum644.53
Range3.32
Interquartile range (IQR)0.675

Descriptive statistics

Standard deviation0.50005327
Coefficient of variation (CV)0.00077807392
Kurtosis-0.11204294
Mean642.68093
Median Absolute Deviation (MAD)0.34
Skewness0.31652589
Sum13259150
Variance0.25005327
MonotonicityNot monotonic
2022-12-23T16:51:45.635042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642.5 190
 
0.9%
642.56 189
 
0.9%
642.53 188
 
0.9%
642.6 184
 
0.9%
642.67 179
 
0.9%
642.44 175
 
0.8%
642.63 175
 
0.8%
642.57 172
 
0.8%
642.64 168
 
0.8%
642.73 167
 
0.8%
Other values (300) 18844
91.3%
ValueCountFrequency (%)
641.21 1
 
< 0.1%
641.25 2
< 0.1%
641.27 3
< 0.1%
641.3 4
< 0.1%
641.31 1
 
< 0.1%
641.32 2
< 0.1%
641.33 2
< 0.1%
641.34 1
 
< 0.1%
641.35 1
 
< 0.1%
641.36 2
< 0.1%
ValueCountFrequency (%)
644.53 2
< 0.1%
644.5 1
< 0.1%
644.47 1
< 0.1%
644.44 1
< 0.1%
644.39 1
< 0.1%
644.37 1
< 0.1%
644.35 1
< 0.1%
644.34 1
< 0.1%
644.31 1
< 0.1%
644.3 2
< 0.1%

sensor_3
Real number (ℝ)

Distinct3012
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1590.5231
Minimum1571.04
Maximum1616.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:45.961701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1571.04
5-th percentile1581.11
Q11586.26
median1590.1
Q31594.38
95-th percentile1601.47
Maximum1616.91
Range45.87
Interquartile range (IQR)8.12

Descriptive statistics

Standard deviation6.1311495
Coefficient of variation (CV)0.0038548006
Kurtosis0.0077618224
Mean1590.5231
Median Absolute Deviation (MAD)4.05
Skewness0.30894581
Sum32814082
Variance37.590994
MonotonicityNot monotonic
2022-12-23T16:51:46.243953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1590.1 27
 
0.1%
1589.76 26
 
0.1%
1589.98 25
 
0.1%
1592.11 25
 
0.1%
1587.86 24
 
0.1%
1584.95 23
 
0.1%
1590.54 23
 
0.1%
1589.08 23
 
0.1%
1589.44 23
 
0.1%
1587.82 22
 
0.1%
Other values (3002) 20390
98.8%
ValueCountFrequency (%)
1571.04 1
< 0.1%
1571.06 1
< 0.1%
1571.84 1
< 0.1%
1571.99 1
< 0.1%
1572.34 1
< 0.1%
1572.4 1
< 0.1%
1572.46 1
< 0.1%
1572.67 1
< 0.1%
1572.76 1
< 0.1%
1572.98 1
< 0.1%
ValueCountFrequency (%)
1616.91 1
< 0.1%
1614.93 1
< 0.1%
1614.72 1
< 0.1%
1613.62 1
< 0.1%
1613.29 1
< 0.1%
1612.88 1
< 0.1%
1612.63 1
< 0.1%
1612.11 1
< 0.1%
1611.92 1
< 0.1%
1611.57 1
< 0.1%

sensor_4
Real number (ℝ)

Distinct4051
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1408.9338
Minimum1382.25
Maximum1441.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:46.605476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1382.25
5-th percentile1395.62
Q11402.36
median1408.04
Q31414.555
95-th percentile1425.67
Maximum1441.49
Range59.24
Interquartile range (IQR)12.195

Descriptive statistics

Standard deviation9.0006048
Coefficient of variation (CV)0.0063882383
Kurtosis-0.16368086
Mean1408.9338
Median Absolute Deviation (MAD)6.04
Skewness0.44319434
Sum29067713
Variance81.010886
MonotonicityNot monotonic
2022-12-23T16:51:46.886707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1409.01 20
 
0.1%
1404.47 18
 
0.1%
1407.15 18
 
0.1%
1407.02 18
 
0.1%
1414.03 18
 
0.1%
1410.54 18
 
0.1%
1403.23 17
 
0.1%
1407.18 16
 
0.1%
1410.57 16
 
0.1%
1401.27 16
 
0.1%
Other values (4041) 20456
99.2%
ValueCountFrequency (%)
1382.25 1
< 0.1%
1385.19 1
< 0.1%
1385.75 1
< 0.1%
1386.29 1
< 0.1%
1386.43 1
< 0.1%
1386.69 1
< 0.1%
1387.16 1
< 0.1%
1387.36 1
< 0.1%
1387.38 1
< 0.1%
1387.5 1
< 0.1%
ValueCountFrequency (%)
1441.49 1
< 0.1%
1438.96 1
< 0.1%
1438.51 1
< 0.1%
1438.41 1
< 0.1%
1438.22 1
< 0.1%
1438.16 1
< 0.1%
1438.1 1
< 0.1%
1437.98 1
< 0.1%
1437.88 1
< 0.1%
1437.81 1
< 0.1%

sensor_5
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.4 KiB
14.62
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters103155
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14.62
2nd row14.62
3rd row14.62
4th row14.62
5th row14.62

Common Values

ValueCountFrequency (%)
14.62 20631
100.0%

Length

2022-12-23T16:51:47.168836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:51:47.403194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
14.62 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82524
80.0%
Other Punctuation 20631
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20631
25.0%
4 20631
25.0%
6 20631
25.0%
2 20631
25.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%

sensor_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.4 KiB
21.61
20225 
21.6
 
406

Length

Max length5
Median length5
Mean length4.9803209
Min length4

Characters and Unicode

Total characters102749
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21.61
2nd row21.61
3rd row21.61
4th row21.61
5th row21.61

Common Values

ValueCountFrequency (%)
21.61 20225
98.0%
21.6 406
 
2.0%

Length

2022-12-23T16:51:47.607671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:51:47.857677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
21.61 20225
98.0%
21.6 406
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82118
79.9%
Other Punctuation 20631
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 40856
49.8%
2 20631
25.1%
6 20631
25.1%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

sensor_7
Real number (ℝ)

Distinct513
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.36771
Minimum549.85
Maximum556.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:48.108377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum549.85
5-th percentile551.74
Q1552.81
median553.44
Q3554.01
95-th percentile554.69
Maximum556.06
Range6.21
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.88509226
Coefficient of variation (CV)0.0015994649
Kurtosis-0.15794922
Mean553.36771
Median Absolute Deviation (MAD)0.6
Skewness-0.39432894
Sum11416529
Variance0.7833883
MonotonicityNot monotonic
2022-12-23T16:51:48.391444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
553.62 116
 
0.6%
553.76 115
 
0.6%
553.72 110
 
0.5%
553.94 110
 
0.5%
553.43 108
 
0.5%
553.74 107
 
0.5%
553.75 106
 
0.5%
554 105
 
0.5%
553.9 104
 
0.5%
553.52 103
 
0.5%
Other values (503) 19547
94.7%
ValueCountFrequency (%)
549.85 1
< 0.1%
550.34 1
< 0.1%
550.35 1
< 0.1%
550.42 1
< 0.1%
550.43 1
< 0.1%
550.48 2
< 0.1%
550.49 1
< 0.1%
550.5 1
< 0.1%
550.51 2
< 0.1%
550.52 1
< 0.1%
ValueCountFrequency (%)
556.06 1
< 0.1%
555.86 1
< 0.1%
555.72 1
< 0.1%
555.7 1
< 0.1%
555.67 1
< 0.1%
555.66 1
< 0.1%
555.64 1
< 0.1%
555.61 1
< 0.1%
555.6 1
< 0.1%
555.58 1
< 0.1%

sensor_8
Real number (ℝ)

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0967
Minimum2387.9
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:48.688769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2387.9
5-th percentile2387.99
Q12388.05
median2388.09
Q32388.14
95-th percentile2388.22
Maximum2388.56
Range0.66
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.070985479
Coefficient of variation (CV)2.9724709 × 10-5
Kurtosis0.33314901
Mean2388.0967
Median Absolute Deviation (MAD)0.05
Skewness0.47941086
Sum49268822
Variance0.0050389382
MonotonicityNot monotonic
2022-12-23T16:51:48.970000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.11 1181
 
5.7%
2388.1 1159
 
5.6%
2388.09 1149
 
5.6%
2388.08 1126
 
5.5%
2388.07 1077
 
5.2%
2388.12 1069
 
5.2%
2388.06 1050
 
5.1%
2388.13 1033
 
5.0%
2388.05 1013
 
4.9%
2388.04 910
 
4.4%
Other values (43) 9864
47.8%
ValueCountFrequency (%)
2387.9 1
 
< 0.1%
2387.91 3
 
< 0.1%
2387.92 9
 
< 0.1%
2387.93 16
 
0.1%
2387.94 33
 
0.2%
2387.95 72
 
0.3%
2387.96 145
 
0.7%
2387.97 201
1.0%
2387.98 339
1.6%
2387.99 426
2.1%
ValueCountFrequency (%)
2388.56 1
 
< 0.1%
2388.52 1
 
< 0.1%
2388.5 1
 
< 0.1%
2388.46 1
 
< 0.1%
2388.44 2
 
< 0.1%
2388.37 1
 
< 0.1%
2388.36 1
 
< 0.1%
2388.35 2
 
< 0.1%
2388.34 13
0.1%
2388.33 13
0.1%

sensor_9
Real number (ℝ)

Distinct6403
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9065.2429
Minimum9021.73
Maximum9244.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:49.284951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9021.73
5-th percentile9042.55
Q19053.1
median9060.66
Q39069.42
95-th percentile9109.98
Maximum9244.59
Range222.86
Interquartile range (IQR)16.32

Descriptive statistics

Standard deviation22.08288
Coefficient of variation (CV)0.0024359942
Kurtosis9.3786813
Mean9065.2429
Median Absolute Deviation (MAD)8.13
Skewness2.5553649
Sum1.8702503 × 108
Variance487.65357
MonotonicityNot monotonic
2022-12-23T16:51:49.566181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9058.88 16
 
0.1%
9060.37 15
 
0.1%
9060.55 15
 
0.1%
9056.86 15
 
0.1%
9063.22 15
 
0.1%
9060.87 15
 
0.1%
9054.54 14
 
0.1%
9061.05 14
 
0.1%
9057.95 14
 
0.1%
9065.47 14
 
0.1%
Other values (6393) 20484
99.3%
ValueCountFrequency (%)
9021.73 1
< 0.1%
9023.85 1
< 0.1%
9024.27 1
< 0.1%
9024.42 1
< 0.1%
9025.22 1
< 0.1%
9025.29 1
< 0.1%
9026.08 1
< 0.1%
9026.17 1
< 0.1%
9026.19 1
< 0.1%
9026.66 1
< 0.1%
ValueCountFrequency (%)
9244.59 1
< 0.1%
9239.76 1
< 0.1%
9228.53 1
< 0.1%
9226.6 1
< 0.1%
9224.87 1
< 0.1%
9224.53 1
< 0.1%
9223.56 1
< 0.1%
9221.31 1
< 0.1%
9220.88 1
< 0.1%
9219.81 1
< 0.1%

sensor_10
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.4 KiB
1.3
20631 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters61893
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.3
2nd row1.3
3rd row1.3
4th row1.3
5th row1.3

Common Values

ValueCountFrequency (%)
1.3 20631
100.0%

Length

2022-12-23T16:51:49.830636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:51:50.049371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.3 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41262
66.7%
Other Punctuation 20631
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20631
50.0%
3 20631
50.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61893
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%

sensor_11
Real number (ℝ)

Distinct159
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.541168
Minimum46.85
Maximum48.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:50.270281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum46.85
5-th percentile47.15
Q147.35
median47.51
Q347.7
95-th percentile48.045
Maximum48.53
Range1.68
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.2670874
Coefficient of variation (CV)0.0056180235
Kurtosis-0.17219188
Mean47.541168
Median Absolute Deviation (MAD)0.18
Skewness0.46932909
Sum980821.84
Variance0.071335679
MonotonicityNot monotonic
2022-12-23T16:51:50.598368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.46 341
 
1.7%
47.57 338
 
1.6%
47.49 332
 
1.6%
47.45 332
 
1.6%
47.47 331
 
1.6%
47.52 326
 
1.6%
47.37 321
 
1.6%
47.48 319
 
1.5%
47.44 318
 
1.5%
47.43 311
 
1.5%
Other values (149) 17362
84.2%
ValueCountFrequency (%)
46.85 1
 
< 0.1%
46.86 3
< 0.1%
46.88 2
 
< 0.1%
46.89 1
 
< 0.1%
46.9 1
 
< 0.1%
46.91 1
 
< 0.1%
46.92 3
< 0.1%
46.93 3
< 0.1%
46.94 6
< 0.1%
46.95 6
< 0.1%
ValueCountFrequency (%)
48.53 1
 
< 0.1%
48.52 1
 
< 0.1%
48.48 1
 
< 0.1%
48.43 1
 
< 0.1%
48.41 4
< 0.1%
48.4 4
< 0.1%
48.39 3
< 0.1%
48.38 1
 
< 0.1%
48.37 2
 
< 0.1%
48.35 5
< 0.1%

sensor_12
Real number (ℝ)

Distinct427
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.41347
Minimum518.69
Maximum523.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:50.896811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum518.69
5-th percentile520.04
Q1520.96
median521.48
Q3521.95
95-th percentile522.5
Maximum523.38
Range4.69
Interquartile range (IQR)0.99

Descriptive statistics

Standard deviation0.73755339
Coefficient of variation (CV)0.0014145269
Kurtosis-0.14491657
Mean521.41347
Median Absolute Deviation (MAD)0.5
Skewness-0.44240724
Sum10757281
Variance0.54398501
MonotonicityNot monotonic
2022-12-23T16:51:51.193666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.63 143
 
0.7%
521.42 136
 
0.7%
521.35 131
 
0.6%
521.56 129
 
0.6%
521.66 126
 
0.6%
521.54 125
 
0.6%
521.69 124
 
0.6%
521.5 123
 
0.6%
521.46 121
 
0.6%
521.43 121
 
0.6%
Other values (417) 19352
93.8%
ValueCountFrequency (%)
518.69 1
< 0.1%
518.83 2
< 0.1%
518.94 1
< 0.1%
518.95 1
< 0.1%
518.98 1
< 0.1%
518.99 1
< 0.1%
519.01 1
< 0.1%
519.02 1
< 0.1%
519.03 1
< 0.1%
519.06 2
< 0.1%
ValueCountFrequency (%)
523.38 2
< 0.1%
523.35 1
< 0.1%
523.31 1
< 0.1%
523.27 1
< 0.1%
523.26 2
< 0.1%
523.25 1
< 0.1%
523.24 1
< 0.1%
523.23 1
< 0.1%
523.21 1
< 0.1%
523.2 1
< 0.1%

sensor_13
Real number (ℝ)

Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0962
Minimum2387.88
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:51.523081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2387.88
5-th percentile2387.99
Q12388.04
median2388.09
Q32388.14
95-th percentile2388.23
Maximum2388.56
Range0.68
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.071918916
Coefficient of variation (CV)3.0115586 × 10-5
Kurtosis0.38724376
Mean2388.0962
Median Absolute Deviation (MAD)0.05
Skewness0.46979242
Sum49268812
Variance0.0051723304
MonotonicityNot monotonic
2022-12-23T16:51:51.803543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.1 1164
 
5.6%
2388.09 1144
 
5.5%
2388.08 1129
 
5.5%
2388.11 1127
 
5.5%
2388.07 1112
 
5.4%
2388.12 1099
 
5.3%
2388.06 1005
 
4.9%
2388.05 987
 
4.8%
2388.13 976
 
4.7%
2388.04 952
 
4.6%
Other values (46) 9936
48.2%
ValueCountFrequency (%)
2387.88 1
 
< 0.1%
2387.89 1
 
< 0.1%
2387.9 1
 
< 0.1%
2387.91 2
 
< 0.1%
2387.92 12
 
0.1%
2387.93 19
 
0.1%
2387.94 54
 
0.3%
2387.95 95
0.5%
2387.96 170
0.8%
2387.97 219
1.1%
ValueCountFrequency (%)
2388.56 1
 
< 0.1%
2388.55 1
 
< 0.1%
2388.54 1
 
< 0.1%
2388.49 1
 
< 0.1%
2388.44 1
 
< 0.1%
2388.39 2
 
< 0.1%
2388.37 3
 
< 0.1%
2388.36 6
< 0.1%
2388.35 7
< 0.1%
2388.34 8
< 0.1%

sensor_14
Real number (ℝ)

Distinct6078
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8143.7527
Minimum8099.94
Maximum8293.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:52.118526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8099.94
5-th percentile8122.505
Q18133.245
median8140.54
Q38148.31
95-th percentile8181.405
Maximum8293.72
Range193.78
Interquartile range (IQR)15.065

Descriptive statistics

Standard deviation19.076176
Coefficient of variation (CV)0.0023424306
Kurtosis8.8546645
Mean8143.7527
Median Absolute Deviation (MAD)7.54
Skewness2.3725536
Sum1.6801376 × 108
Variance363.90049
MonotonicityNot monotonic
2022-12-23T16:51:52.416886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8138.89 17
 
0.1%
8141.85 17
 
0.1%
8136.89 16
 
0.1%
8140.79 15
 
0.1%
8140.65 15
 
0.1%
8140.49 15
 
0.1%
8140.33 15
 
0.1%
8140.89 15
 
0.1%
8136.69 15
 
0.1%
8140.97 15
 
0.1%
Other values (6068) 20476
99.2%
ValueCountFrequency (%)
8099.94 1
< 0.1%
8101.49 1
< 0.1%
8102.82 1
< 0.1%
8103.27 1
< 0.1%
8103.77 1
< 0.1%
8103.98 1
< 0.1%
8104.46 1
< 0.1%
8104.78 1
< 0.1%
8104.82 1
< 0.1%
8105.22 1
< 0.1%
ValueCountFrequency (%)
8293.72 1
< 0.1%
8290.25 1
< 0.1%
8289.63 1
< 0.1%
8288.26 1
< 0.1%
8282.5 1
< 0.1%
8279.86 1
< 0.1%
8279.79 1
< 0.1%
8276.2 1
< 0.1%
8274.65 1
< 0.1%
8273.15 1
< 0.1%

sensor_15
Real number (ℝ)

Distinct1918
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4421456
Minimum8.3249
Maximum8.5848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:52.745006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.3249
5-th percentile8.3859
Q18.4149
median8.4389
Q38.4656
95-th percentile8.511
Maximum8.5848
Range0.2599
Interquartile range (IQR)0.0507

Descriptive statistics

Standard deviation0.037505038
Coefficient of variation (CV)0.0044425955
Kurtosis-0.12143
Mean8.4421456
Median Absolute Deviation (MAD)0.0252
Skewness0.38825858
Sum174169.91
Variance0.0014066279
MonotonicityNot monotonic
2022-12-23T16:51:53.042301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4309 38
 
0.2%
8.4318 37
 
0.2%
8.4468 36
 
0.2%
8.4442 35
 
0.2%
8.4128 34
 
0.2%
8.4453 32
 
0.2%
8.4446 32
 
0.2%
8.4371 31
 
0.2%
8.4209 31
 
0.2%
8.4226 31
 
0.2%
Other values (1908) 20294
98.4%
ValueCountFrequency (%)
8.3249 1
< 0.1%
8.3279 1
< 0.1%
8.3303 1
< 0.1%
8.3358 2
< 0.1%
8.3365 1
< 0.1%
8.3387 1
< 0.1%
8.34 1
< 0.1%
8.3409 1
< 0.1%
8.3427 1
< 0.1%
8.3428 1
< 0.1%
ValueCountFrequency (%)
8.5848 1
< 0.1%
8.5836 1
< 0.1%
8.5678 1
< 0.1%
8.5671 1
< 0.1%
8.5668 1
< 0.1%
8.5665 1
< 0.1%
8.5654 1
< 0.1%
8.5648 1
< 0.1%
8.5646 1
< 0.1%
8.5641 1
< 0.1%

sensor_16
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.4 KiB
0.03
20631 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters82524
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.03
2nd row0.03
3rd row0.03
4th row0.03
5th row0.03

Common Values

ValueCountFrequency (%)
0.03 20631
100.0%

Length

2022-12-23T16:51:53.292284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:51:53.529160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61893
75.0%
Other Punctuation 20631
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41262
66.7%
3 20631
33.3%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82524
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

sensor_17
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393.21065
Minimum388
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:53.685395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum388
5-th percentile391
Q1392
median393
Q3394
95-th percentile396
Maximum400
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.548763
Coefficient of variation (CV)0.0039387616
Kurtosis-0.039174043
Mean393.21065
Median Absolute Deviation (MAD)1
Skewness0.35312566
Sum8112329
Variance2.3986669
MonotonicityNot monotonic
2022-12-23T16:51:53.936067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
393 5445
26.4%
392 4578
22.2%
394 4063
19.7%
395 2339
11.3%
391 2022
 
9.8%
396 1185
 
5.7%
390 452
 
2.2%
397 436
 
2.1%
398 72
 
0.3%
389 30
 
0.1%
Other values (3) 9
 
< 0.1%
ValueCountFrequency (%)
388 1
 
< 0.1%
389 30
 
0.1%
390 452
 
2.2%
391 2022
 
9.8%
392 4578
22.2%
393 5445
26.4%
394 4063
19.7%
395 2339
11.3%
396 1185
 
5.7%
397 436
 
2.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
399 7
 
< 0.1%
398 72
 
0.3%
397 436
 
2.1%
396 1185
 
5.7%
395 2339
11.3%
394 4063
19.7%
393 5445
26.4%
392 4578
22.2%
391 2022
 
9.8%

sensor_18
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.4 KiB
2388
20631 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters82524
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2388
2nd row2388
3rd row2388
4th row2388
5th row2388

Common Values

ValueCountFrequency (%)
2388 20631
100.0%

Length

2022-12-23T16:51:54.170223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:51:54.410253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2388 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82524
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82524
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

sensor_19
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.4 KiB
100.0
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters103155
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 20631
100.0%

Length

2022-12-23T16:51:54.594649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-23T16:51:54.813402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82524
80.0%
Other Punctuation 20631
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61893
75.0%
1 20631
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

sensor_20
Real number (ℝ)

Distinct120
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.816271
Minimum38.14
Maximum39.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:55.047648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum38.14
5-th percentile38.49
Q138.7
median38.83
Q338.95
95-th percentile39.09
Maximum39.43
Range1.29
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.18074643
Coefficient of variation (CV)0.0046564604
Kurtosis-0.11282911
Mean38.816271
Median Absolute Deviation (MAD)0.12
Skewness-0.3584452
Sum800818.48
Variance0.032669271
MonotonicityNot monotonic
2022-12-23T16:51:55.344520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.86 485
 
2.4%
38.89 476
 
2.3%
38.82 472
 
2.3%
38.87 460
 
2.2%
38.85 458
 
2.2%
38.83 457
 
2.2%
38.84 455
 
2.2%
38.88 452
 
2.2%
38.81 447
 
2.2%
38.8 447
 
2.2%
Other values (110) 16022
77.7%
ValueCountFrequency (%)
38.14 1
 
< 0.1%
38.16 1
 
< 0.1%
38.18 1
 
< 0.1%
38.19 1
 
< 0.1%
38.2 1
 
< 0.1%
38.21 1
 
< 0.1%
38.22 3
 
< 0.1%
38.23 5
< 0.1%
38.24 7
< 0.1%
38.25 9
< 0.1%
ValueCountFrequency (%)
39.43 1
 
< 0.1%
39.41 1
 
< 0.1%
39.34 1
 
< 0.1%
39.32 1
 
< 0.1%
39.31 2
 
< 0.1%
39.3 2
 
< 0.1%
39.29 3
 
< 0.1%
39.28 1
 
< 0.1%
39.27 10
< 0.1%
39.26 7
< 0.1%

sensor_21
Real number (ℝ)

Distinct4745
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.289705
Minimum22.8942
Maximum23.6184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:55.675653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22.8942
5-th percentile23.09345
Q123.2218
median23.2979
Q323.3668
95-th percentile23.4535
Maximum23.6184
Range0.7242
Interquartile range (IQR)0.145

Descriptive statistics

Standard deviation0.10825087
Coefficient of variation (CV)0.0046480139
Kurtosis-0.11703945
Mean23.289705
Median Absolute Deviation (MAD)0.0724
Skewness-0.35037496
Sum480489.91
Variance0.011718252
MonotonicityNot monotonic
2022-12-23T16:51:55.997488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.3222 23
 
0.1%
23.3029 17
 
0.1%
23.2896 16
 
0.1%
23.3725 16
 
0.1%
23.371 15
 
0.1%
23.3491 15
 
0.1%
23.3497 15
 
0.1%
23.3315 15
 
0.1%
23.3002 15
 
0.1%
23.3309 15
 
0.1%
Other values (4735) 20469
99.2%
ValueCountFrequency (%)
22.8942 1
< 0.1%
22.9071 1
< 0.1%
22.9122 1
< 0.1%
22.9305 1
< 0.1%
22.9333 1
< 0.1%
22.9337 1
< 0.1%
22.9364 1
< 0.1%
22.9396 2
< 0.1%
22.9398 1
< 0.1%
22.9402 1
< 0.1%
ValueCountFrequency (%)
23.6184 1
< 0.1%
23.6127 1
< 0.1%
23.6064 1
< 0.1%
23.6005 1
< 0.1%
23.5983 1
< 0.1%
23.589 1
< 0.1%
23.5862 2
< 0.1%
23.5858 1
< 0.1%
23.5825 1
< 0.1%
23.5791 1
< 0.1%

RUL
Real number (ℝ)

Distinct362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.80786
Minimum0
Maximum361
Zeros100
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size322.4 KiB
2022-12-23T16:51:56.315757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q151
median103
Q3155
95-th percentile229
Maximum361
Range361
Interquartile range (IQR)104

Descriptive statistics

Standard deviation68.88099
Coefficient of variation (CV)0.63892363
Kurtosis-0.2185391
Mean107.80786
Median Absolute Deviation (MAD)52
Skewness0.49990397
Sum2224184
Variance4744.5908
MonotonicityNot monotonic
2022-12-23T16:51:56.611051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 100
 
0.5%
61 100
 
0.5%
70 100
 
0.5%
69 100
 
0.5%
68 100
 
0.5%
67 100
 
0.5%
66 100
 
0.5%
65 100
 
0.5%
64 100
 
0.5%
63 100
 
0.5%
Other values (352) 19631
95.2%
ValueCountFrequency (%)
0 100
0.5%
1 100
0.5%
2 100
0.5%
3 100
0.5%
4 100
0.5%
5 100
0.5%
6 100
0.5%
7 100
0.5%
8 100
0.5%
9 100
0.5%
ValueCountFrequency (%)
361 1
< 0.1%
360 1
< 0.1%
359 1
< 0.1%
358 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
355 1
< 0.1%
354 1
< 0.1%
353 1
< 0.1%
352 1
< 0.1%

Interactions

2022-12-23T16:51:27.915985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:47:20.124587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:49:57.170572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:02.643385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:07.959342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:13.398482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:18.512838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:23.412750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:28.846562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:33.935119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:39.168461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:44.493091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:49.744828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:54.830910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:00.200175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:06.242918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:11.415050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:16.666880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:22.471219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:28.216706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:47:21.439263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:49:57.438229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:02.882229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:08.229114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:13.651130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:18.760072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:23.657034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:29.102577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:34.202955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:39.441186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:44.734566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:50.020805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:55.090407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:00.467767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:06.525682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:11.688313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:16.934359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:22.788587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:28.466021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:47:21.730024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:49:57.732792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:03.163559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:08.506350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:13.914351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:19.026685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:23.929235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:29.379611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:34.479826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:39.704197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:45.017891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:50.258338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:55.374681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:00.759655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:06.824988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:11.958645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:17.250789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:23.100321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:29.360082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:47:22.045349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:49:58.021273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:03.426183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:08.758905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:14.192195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:19.286083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:24.190068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:29.645666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:34.758486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:39.971567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:45.301449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:50.528549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:55.663926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:01.024274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:07.086235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:12.288991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:17.529237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:23.385508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:29.656080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:47:22.349998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:49:58.282537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:03.719496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:09.041816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:14.467589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-12-23T16:51:20.875031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:26.530869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:32.713559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:49:56.089837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:01.300153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:06.859681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:12.040799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:17.418592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:22.372580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:27.765799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:32.847152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:38.040427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:43.433533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:48.642092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:53.786184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:59.020268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:05.092851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:10.274812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:15.540042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:21.173153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:26.780340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:32.980189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:49:56.355714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:01.583667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:07.141241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:12.314683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:17.695891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:22.607789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:28.031129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:33.120487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:38.322412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:43.697072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:48.917087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:54.038128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:59.290618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:05.381698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:10.549747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:15.812796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:21.540014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:27.064907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:33.284327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:49:56.635523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:01.855972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:07.417324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:12.569190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:17.964242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:22.857754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:28.300486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:33.386987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:38.589919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:43.951165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:49.185478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:54.299534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:59.584585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:05.657672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:10.799758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:16.089719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:21.855066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:27.345021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:33.562053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:49:56.904886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:02.349578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:07.691585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:12.848846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:18.245894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:23.140720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:28.576863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:33.660726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:38.874511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:44.221967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:49.476680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:54.573254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:50:59.869191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:05.939687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:11.095849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:16.402626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:22.178545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-23T16:51:27.621166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-12-23T16:51:57.097849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-23T16:51:57.898316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-23T16:51:58.588284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-23T16:52:00.027504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-23T16:52:00.655712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-23T16:51:34.012905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-23T16:51:35.305978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unit_numbertime_cyclesOS_1OS_2OS_3sensor_1sensor_2sensor_3sensor_4sensor_5sensor_6sensor_7sensor_8sensor_9sensor_10sensor_11sensor_12sensor_13sensor_14sensor_15sensor_16sensor_17sensor_18sensor_19sensor_20sensor_21RUL
011-0.0007-0.0004100.0518.67641.821589.701400.6014.6221.61554.362388.069046.191.347.47521.662388.028138.628.41950.033922388100.039.0623.4190191
1120.0019-0.0003100.0518.67642.151591.821403.1414.6221.61553.752388.049044.071.347.49522.282388.078131.498.43180.033922388100.039.0023.4236190
213-0.00430.0003100.0518.67642.351587.991404.2014.6221.61554.262388.089052.941.347.27522.422388.038133.238.41780.033902388100.038.9523.3442189
3140.00070.0000100.0518.67642.351582.791401.8714.6221.61554.452388.119049.481.347.13522.862388.088133.838.36820.033922388100.038.8823.3739188
415-0.0019-0.0002100.0518.67642.371582.851406.2214.6221.61554.002388.069055.151.347.28522.192388.048133.808.42940.033932388100.038.9023.4044187
516-0.0043-0.0001100.0518.67642.101584.471398.3714.6221.61554.672388.029049.681.347.16521.682388.038132.858.41080.033912388100.038.9823.3669186
6170.00100.0001100.0518.67642.481592.321397.7714.6221.61554.342388.029059.131.347.36522.322388.038132.328.39740.033922388100.039.1023.3774185
718-0.00340.0003100.0518.67642.561582.961400.9714.6221.61553.852388.009040.801.347.24522.472388.038131.078.40760.033912388100.038.9723.3106184
8190.00080.0001100.0518.67642.121590.981394.8014.6221.61553.692388.059046.461.347.29521.792388.058125.698.37280.033922388100.039.0523.4066183
9110-0.00330.0001100.0518.67641.711591.241400.4614.6221.61553.592388.059051.701.347.03521.792388.068129.388.42860.033932388100.038.9523.4694182
Unit_numbertime_cyclesOS_1OS_2OS_3sensor_1sensor_2sensor_3sensor_4sensor_5sensor_6sensor_7sensor_8sensor_9sensor_10sensor_11sensor_12sensor_13sensor_14sensor_15sensor_16sensor_17sensor_18sensor_19sensor_20sensor_21RUL
20621100191-0.0005-0.0000100.0518.67643.691610.871427.1914.6221.61551.782388.269068.901.348.07519.802388.288143.568.50920.033982388100.038.3923.12189
20622100192-0.00090.0001100.0518.67643.531601.231419.4814.6221.61551.142388.179060.451.348.18520.592388.218143.468.48920.033972388100.038.5623.07708
20623100193-0.00010.0002100.0518.67643.091599.811428.9314.6221.61552.042388.299067.571.348.19520.112388.198142.028.54240.033972388100.038.4723.02307
20624100194-0.00110.0003100.0518.67643.721597.291427.4114.6221.61551.992388.239068.851.348.12519.552388.228139.678.52150.033942388100.038.3823.13246
20625100195-0.0002-0.0001100.0518.67643.411600.041431.9014.6221.61551.422388.239069.691.348.22519.712388.288142.908.55190.033942388100.038.1423.19235
20626100196-0.0004-0.0003100.0518.67643.491597.981428.6314.6221.61551.432388.199065.521.348.07519.492388.268137.608.49560.033972388100.038.4922.97354
20627100197-0.0016-0.0005100.0518.67643.541604.501433.5814.6221.61550.862388.239065.111.348.04519.682388.228136.508.51390.033952388100.038.3023.15943
206281001980.00040.0000100.0518.67643.421602.461428.1814.6221.61550.942388.249065.901.348.09520.012388.248141.058.56460.033982388100.038.4422.93332
20629100199-0.00110.0003100.0518.67643.231605.261426.5314.6221.61550.682388.259073.721.348.39519.672388.238139.298.53890.033952388100.038.2923.06401
20630100200-0.0032-0.0005100.0518.67643.851600.381432.1414.6221.61550.792388.269061.481.348.20519.302388.268137.338.50360.033962388100.038.3723.05220